  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 show that Oracle Dice index measurements are
insensitive or even misleading in measuring this property. We demonstrate the
use of the proposed procedure on three interactive segmentation models and
subsets of two large image segmentation datasets.
