  This study investigates the potential of eye-tracking technology and the
Segment Anything Model (SAM) to design a collaborative human-computer
interaction system that automates medical image segmentation. We present the
\textbf{GazeSAM} system to enable radiologists to collect segmentation masks by
simply looking at the region of interest during image diagnosis. The proposed
system tracks radiologists' eye movement and utilizes the eye-gaze data as the
input prompt for SAM, which automatically generates the segmentation mask in
real time. This study is the first work to leverage the power of eye-tracking
technology and SAM to enhance the efficiency of daily clinical practice.
Moreover, eye-gaze data coupled with image and corresponding segmentation
labels can be easily recorded for further advanced eye-tracking research. The
code is available in \url{https://github.com/ukaukaaaa/GazeSAM}.
