Knowing When Not to Predict: Self Supervised Learning and Abstention for Safer {DR} Screening

Self-supervised learning ({SSL}) is now a standard way to pretrain medical image models, but performance is still mostly judged by downstream accuracy. For safety-critical screening tasks such as diabetic retinopathy grading, this is not enough: a model must also know when its predictions are unreliable and defer uncertain cases for clinical review. In this work, we examine how the length of {SSL} pretraining influences calibrated confidence and confidence-based abstention. We evaluate multiple {SSL} checkpoints under a fixed fine-tuning protocol and assess calibrated confidence, coverage, selective accuracy, and selective macro-F1. Across datasets and data regimes, {SSL} pretraining improves selective prediction compared to training from scratch. Unlike prior {SSL} studies that primarily evaluate downstream accuracy or {AUROC}, we analyze how {SSL} pretraining duration influences confidence behavior under calibrated confidence-based abstention. However, once accuracy saturates, selective performance can still change markedly across checkpoints, and longer pretraining does not consistently improve reliability. These results underscore the importance of abstention-aware evaluation and suggest that pretraining length should be treated as an important reliability-related design choice rather than only a computational detail. Code is available at {GitHub}.