For artificial intelligence to be safely deployed in high-risk domains, it must reliably know its limits. Selective predic- tion, or learning with a reject option, addresses this by en- abling a model to abstain from prediction on inputs it deems unreliable, deferring them to a human expert. While deep en- sembles have emerged as a leading approach for uncertainty estimation, their potential is often squandered by rejection methods that rely on static thresholds applied to the mean prediction. In this paper, we propose to learn a dynamic rejec- tion policy directly from the rich behavioral signals of the en- semble itself. Our framework, DEGRE (Dynamic Ensembles Gating for REjection), is a novel meta-learning approach that trains a lightweight gating network on the ensemble’s con- sensus confidence and its internal disagreement (variance)— to explicitly discriminate between correct and incorrect pre- dictions. Through rigorous evaluation across twelve diverse medical imaging benchmarks (MRI, X-ray, CT), DEGRE sig- nificantly advances selective prediction, achieving an aver- age risk-coverage (AURC) reduction of 68.2% compared to the standard ensemble baseline. By providing a more reli- able method for a model to recognize its own limitations, this learned, adaptive rejection mechanism provides the ro- bust self-awareness necessary for true AI-in-the-loop (AI2L) systems, paving the way for the safe and responsible integra- tion of AI into critical clinical workflows.