The proposed class ambiguity indices, namely the Boundary Pixel Ratio (BPR) and Mahalanobis Distance for Flood-segmentation (MDF), can be extended to handle multi-class segmentation tasks by adapting them to accommodate more than two classes. In the case of multi-class segmentation, the BPR can be modified to calculate the proportion of boundary pixels for each class combination. This would involve considering the boundaries between all pairs of classes in the image and calculating the ratio of boundary pixels for each pair. Similarly, the MDF can be adjusted to compute the Mahalanobis distance between the average pixel values of different class combinations, providing a measure of semantic ambiguity between various class pairs. By extending these indices to multi-class scenarios, they can help quantify the ambiguity and uncertainty in segmentation tasks involving multiple classes, providing valuable insights for model training and data selection.