  Medical image analysis using deep learning is often challenged by limited
labeled data and high annotation costs. Fine-tuning the entire network in
label-limited scenarios can lead to overfitting and suboptimal performance.
Recently, prompt tuning has emerged as a more promising technique that
introduces a few additional tunable parameters as prompts to a task-agnostic
pre-trained model, and updates only these parameters using supervision from
limited labeled data while keeping the pre-trained model unchanged. However,
previous work has overlooked the importance of selective labeling in downstream
tasks, which aims to select the most valuable downstream samples for annotation
to achieve the best performance with minimum annotation cost. To address this,
we propose a framework that combines selective labeling with prompt tuning
(SLPT) to boost performance in limited labels. Specifically, we introduce a
feature-aware prompt updater to guide prompt tuning and a TandEm Selective
LAbeling (TESLA) strategy. TESLA includes unsupervised diversity selection and
supervised selection using prompt-based uncertainty. In addition, we propose a
diversified visual prompt tuning strategy to provide multi-prompt-based
discrepant predictions for TESLA. We evaluate our method on liver tumor
segmentation and achieve state-of-the-art performance, outperforming
traditional fine-tuning with only 6% of tunable parameters, also achieving 94%
of full-data performance by labeling only 5% of the data.
