diff --git a/albumentations/augmentations/crops/transforms.py b/albumentations/augmentations/crops/transforms.py
index 982872706..5681d78c7 100644
--- a/albumentations/augmentations/crops/transforms.py
+++ b/albumentations/augmentations/crops/transforms.py
@@ -175,6 +175,26 @@ def apply(
             )
         return super().apply(img, crop_coords, **params)
 
+    def apply_to_mask(
+        self,
+        mask: np.ndarray,
+        crop_coords: Any,
+        **params: Any,
+    ) -> np.ndarray:
+        pad_params = params.get("pad_params")
+        if pad_params is not None:
+            mask = fgeometric.pad_with_params(
+                mask,
+                pad_params["pad_top"],
+                pad_params["pad_bottom"],
+                pad_params["pad_left"],
+                pad_params["pad_right"],
+                border_mode=self.border_mode,
+                value=self.fill_mask,
+            )
+        # Note' that super().apply would apply the padding twice as it is looped to this.apply
+        return BaseCrop.apply(self, mask, crop_coords=crop_coords, **params)
+
     def apply_to_bboxes(
         self,
         bboxes: np.ndarray,
diff --git a/tests/test_crop.py b/tests/test_crop.py
index c1c7fa13f..2b55dcd75 100644
--- a/tests/test_crop.py
+++ b/tests/test_crop.py
@@ -211,3 +211,27 @@ def test_pad_position_equivalence(
         result2["keypoints"],
         err_msg=f"Keypoints don't match for position {pad_position}"
     )
+
+def test_base_crop_and_pad_fill():
+    # tests whether BaseCropAndPad usues correct values for constant borders
+    c = A.CenterCrop(4, 4, pad_if_needed=True, fill=100, fill_mask=200)
+    c1 = A.CenterCrop(4, 4, pad_if_needed=True, fill=201)
+
+    im = np.zeros((2, 6, 3)).astype(np.float32)
+    msk = np.zeros((2, 6)).astype(np.uint8)
+
+    out = c(image=im, mask=msk)
+    out1 = c1(image=im, mask=msk)
+
+    expected_img = np.ones((4, 4, 3)).astype(np.float32)
+    expected_img[1:3, ...] = 0
+
+    expected_msk = np.ones((4, 4)).astype(np.uint8)
+    expected_msk[1:3, ...] = 0
+
+    assert np.all(out["image"] == expected_img * 100)
+    assert np.all(out["mask"] == expected_msk * 200)
+
+
+    assert np.all(out1["image"] == expected_img * 201)
+    assert np.all(out1["mask"] == expected_msk * 0)  # 0 is the default for fill_mask