How Many Labels Are Enough? ALDA: Active Learning Deployment Advisor for Medical Image Classification

The paper introduces ALDA, a deployment-oriented decision framework for selecting active-learning strategies in medical image classification. From a short pilot phase, it fits a parametric learning curve for each candidate strategy, estimates whether the strategy can reach a required clinical performance target, and predicts the expert annotation budget needed. ALDA also defines a deployment window to measure sensitivity to uncertainty in the clinical threshold. Across four medical imaging classification domains, the authors report that a pilot using 15–30% of the intended budget can identify the deployment-optimal strategy, with annotation-cost reductions of up to 82% versus a poor strategy choice.