  The recently released Segment Anything Model (SAM) has shown powerful
zero-shot segmentation capabilities through a semi-automatic annotation setup
in which the user can provide a prompt in the form of clicks or bounding boxes.
There is growing interest around applying this to medical imaging, where the
cost of obtaining expert annotations is high, privacy restrictions may limit
sharing of patient data, and model generalisation is often poor. However, there
are large amounts of inherent uncertainty in medical images, due to unclear
object boundaries, low-contrast media, and differences in expert labelling
style. Currently, SAM is known to struggle in a zero-shot setting to adequately
annotate the contours of the structure of interest in medical images, where the
uncertainty is often greatest, thus requiring significant manual correction. To
mitigate this, we introduce \textbf{Sim}ulated Interaction for \textbf{S}egment
\textbf{A}nything \textbf{M}odel (\textsc{\textbf{SimSAM}}), an approach that
leverages simulated user interaction to generate an arbitrary number of
candidate masks, and uses a novel aggregation approach to output the most
compatible mask. Crucially, our method can be used during inference directly on
top of SAM, without any additional training requirement. Quantitatively, we
evaluate our method across three publicly available medical imaging datasets,
and find that our approach leads to up to a 15.5\% improvement in contour
segmentation accuracy compared to zero-shot SAM. Our code is available at
\url{https://github.com/BenjaminTowle/SimSAM}.
