  Eye gaze that reveals human observational patterns has increasingly been
incorporated into solutions for vision tasks. Despite recent explorations on
leveraging gaze to aid deep networks, few studies exploit gaze as an efficient
annotation approach for medical image segmentation which typically entails
heavy annotating costs. In this paper, we propose to collect dense weak
supervision for medical image segmentation with a gaze annotation scheme. To
train with gaze, we propose a multi-level framework that trains multiple
networks from discriminative human attention, simulated with a set of
pseudo-masks derived by applying hierarchical thresholds on gaze heatmaps.
Furthermore, to mitigate gaze noise, a cross-level consistency is exploited to
regularize overfitting noisy labels, steering models toward clean patterns
learned by peer networks. The proposed method is validated on two public
medical datasets of polyp and prostate segmentation tasks. We contribute a
high-quality gaze dataset entitled GazeMedSeg as an extension to the popular
medical segmentation datasets. To the best of our knowledge, this is the first
gaze dataset for medical image segmentation. Our experiments demonstrate that
gaze annotation outperforms previous label-efficient annotation schemes in
terms of both performance and annotation time. Our collected gaze data and code
are available at: https://github.com/med-air/GazeMedSeg.
