Does SAM dream of EIG? Characterizing interactive segmenter performance using expected information gain

We introduce an assessment procedure for interactive segmentation models. Based on concepts from Bayesian Experimental Design, the procedure measures a model’s understanding of point prompts and their correspondence with the desired segmentation mask. We demonstrate the use of the proposed procedure on out-of-the-box SAM and three medical datasets.