  Interactive segmentation model leverages prompts from users to produce robust
segmentation. This advancement is facilitated by prompt engineering, where
interactive prompts serve as strong priors during test-time. However, this is
an inherently subjective and hard-to-reproduce process. The variability in user
expertise and inherently ambiguous boundaries in medical images can lead to
inconsistent prompt selections, potentially affecting segmentation accuracy.
This issue has not yet been extensively explored for medical imaging. In this
paper, we assess the test-time variability for interactive medical image
segmentation with diverse point prompts. For a given target region, the point
is classified into three sub-regions: boundary, margin, and center. Our goal is
to identify a straightforward and efficient approach for optimal prompt
selection during test-time based on three considerations: (1) benefits of
additional prompts, (2) effects of prompt placement, and (3) strategies for
optimal prompt selection. We conduct extensive experiments on the public
Medical Segmentation Decathlon dataset for challenging colon tumor segmentation
task. We suggest an optimal strategy for prompt selection during test-time,
supported by comprehensive results. The code is publicly available at
https://github.com/MedICL-VU/variability
