  Interactive segmentation plays a crucial role in accelerating the annotation,
particularly in domains requiring specialized expertise such as nuclear
medicine. For example, annotating lesions in whole-body Positron Emission
Tomography (PET) images can require over an hour per volume. While previous
works evaluate interactive segmentation models through either real user studies
or simulated annotators, both approaches present challenges. Real user studies
are expensive and often limited in scale, while simulated annotators, also
known as robot users, tend to overestimate model performance due to their
idealized nature. To address these limitations, we introduce four evaluation
metrics that quantify the user shift between real and simulated annotators. In
an initial user study involving four annotators, we assess existing robot users
using our proposed metrics and find that robot users significantly deviate in
performance and annotation behavior compared to real annotators. Based on these
findings, we propose a more realistic robot user that reduces the user shift by
incorporating human factors such as click variation and inter-annotator
disagreement. We validate our robot user in a second user study, involving four
other annotators, and show it consistently reduces the simulated-to-real user
shift compared to traditional robot users. By employing our robot user, we can
conduct more large-scale and cost-efficient evaluations of interactive
segmentation models, while preserving the fidelity of real user studies. Our
implementation is based on MONAI Label and will be made publicly available.
