  Automated nodule segmentation is essential for computer-assisted diagnosis in
ultrasound images. Nevertheless, most existing methods depend on precise
pixel-level annotations by medical professionals, a process that is both costly
and labor-intensive. Recently, segmentation foundation models like SAM have
shown impressive generalizability on natural images, suggesting their potential
as pseudo-labelers. However, accurate prompts remain crucial for their success
in medical images. In this work, we devise a novel weakly supervised framework
that effectively utilizes the segmentation foundation model to generate
pseudo-labels from aspect ration annotations for automatic nodule segmentation.
Specifically, we develop three types of bounding box prompts based on scalable
shape priors, followed by an adaptive pseudo-label selection module to fully
exploit the prediction capabilities of the foundation model for nodules. We
also present a SAM-driven uncertainty-aware cross-teaching strategy. This
approach integrates SAM-based uncertainty estimation and label-space
perturbations into cross-teaching to mitigate the impact of pseudo-label
inaccuracies on model training. Extensive experiments on two clinically
collected ultrasound datasets demonstrate the superior performance of our
proposed method.
