diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py
index dfdeba2a90554..046df233a5436 100644
--- a/pandas/core/arrays/interval.py
+++ b/pandas/core/arrays/interval.py
@@ -692,8 +692,8 @@ def __setitem__(self, key, value) -> None:
         if self._readonly:
             raise ValueError("Cannot modify read-only array")
 
-        value_left, value_right = self._validate_setitem_value(value)
         key = check_array_indexer(self, key)
+        value_left, value_right = self._validate_setitem_value(value)
 
         self._left[key] = value_left
         self._right[key] = value_right
@@ -900,7 +900,7 @@ def fillna(self, value, limit: int | None = None, copy: bool = True) -> Self:
         if limit is not None:
             raise ValueError("limit must be None")
 
-        value_left, value_right = self._validate_scalar(value)
+        value_left, value_right = self._validate_setitem_value(value)
 
         left = self.left.fillna(value=value_left)
         right = self.right.fillna(value=value_right)
@@ -1169,7 +1169,8 @@ def _validate_scalar(self, value):
         if isinstance(value, Interval):
             self._check_closed_matches(value, name="value")
             left, right = value.left, value.right
-            # TODO: check subdtype match like _validate_setitem_value?
+            self.left._validate_fill_value(left)
+            self.left._validate_fill_value(right)
         elif is_valid_na_for_dtype(value, self.left.dtype):
             # GH#18295
             left = right = self.left._na_value
@@ -1180,27 +1181,19 @@ def _validate_scalar(self, value):
         return left, right
 
     def _validate_setitem_value(self, value):
+        if is_list_like(value):
+            return self._validate_listlike(value)
+
+        left, right = self._validate_scalar(value)
+
         if is_valid_na_for_dtype(value, self.left.dtype):
-            # na value: need special casing to set directly on numpy arrays
-            value = self.left._na_value
             if is_integer_dtype(self.dtype.subtype):
                 # can't set NaN on a numpy integer array
                 # GH#45484 TypeError, not ValueError, matches what we get with
                 #  non-NA un-holdable value.
                 raise TypeError("Cannot set float NaN to integer-backed IntervalArray")
-            value_left, value_right = value, value
-
-        elif isinstance(value, Interval):
-            # scalar interval
-            self._check_closed_matches(value, name="value")
-            value_left, value_right = value.left, value.right
-            self.left._validate_fill_value(value_left)
-            self.left._validate_fill_value(value_right)
 
-        else:
-            return self._validate_listlike(value)
-
-        return value_left, value_right
+        return left, right
 
     # ---------------------------------------------------------------------
     # Rendering Methods
diff --git a/pandas/tests/arrays/interval/test_interval.py b/pandas/tests/arrays/interval/test_interval.py
index 4b1f666c7e63b..cc37a9bc8793c 100644
--- a/pandas/tests/arrays/interval/test_interval.py
+++ b/pandas/tests/arrays/interval/test_interval.py
@@ -157,7 +157,7 @@ def test_set_na(self, left_right_dtypes):
         result = IntervalArray.from_arrays(left, right, copy=True)
 
         if result.dtype.subtype.kind not in ["m", "M"]:
-            msg = "'value' should be an interval type, got <.*NaTType'> instead."
+            msg = "can only insert Interval objects and NA into an IntervalArray"
             with pytest.raises(TypeError, match=msg):
                 result[0] = pd.NaT
         if result.dtype.subtype.kind in ["i", "u"]:
@@ -266,6 +266,6 @@ def test_min_max(self, left_right_dtypes, index_or_series_or_array):
 
 def test_fillna_non_scalar_raises():
     arr = IntervalArray.from_tuples([None, (0, 1)])
-    msg = "can only insert Interval objects and NA into an IntervalArray"
+    msg = "'value' should be an interval type, got <class 'list'> instead."
     with pytest.raises(TypeError, match=msg):
         arr.fillna([1, 1])