  Label scarcity has been a long-standing issue for biomedical image
segmentation, due to high annotation costs and professional requirements.
Recently, active learning (AL) strategies strive to reduce annotation costs by
querying a small portion of data for annotation, receiving much traction in the
field of medical imaging. However, most of the existing AL methods have to
initialize models with some randomly selected samples followed by active
selection based on various criteria, such as uncertainty and diversity. Such
random-start initialization methods inevitably introduce under-value redundant
samples and unnecessary annotation costs. For the purpose of addressing the
issue, we propose a novel self-supervised assisted active learning framework in
the cold-start setting, in which the segmentation model is first warmed up with
self-supervised learning (SSL), and then SSL features are used for sample
selection via latent feature clustering without accessing labels. We assess our
proposed methodology on skin lesions segmentation task. Extensive experiments
demonstrate that our approach is capable of achieving promising performance
with substantial improvements over existing baselines.
