Breast cancer is the leading cause of cancer-related mortality in women worldwide (2.3 million new cases with over 600,000 deaths in 2022). While accurate segmentation of radiological images is crucial for early diagnosis, real-world deployment also requires knowing when a model’s prediction can be trusted. This preliminary study explores the integration of trustworthiness into lesion segmentation for 3D Digital Breast Tomosynthesis using an ensemble of Attention-UNet models to estimate pixel-wise reliability and generate interpretable confidence maps. A novel dataset of annotated images is used to train an attention-based U-net model on 2D slices, using 5-fold cross-validation and stratified patient splits. To model predictive uncertainty, an ensemble of five independently trained networks is introduced, aggregating predictions through the pixel-wise median and computing standard deviation as a proxy for reliability. This enables the segmentation to be partitioned into high- and low-confidence zones. The Attention U-Net presents a valuable performance (74.1% Dice Score) and a high degree of precision (85.1%). Reliability maps reveal structured uncertainty, primarily at lesion boundaries, enabling confidence-based filtering. Notably, segmentation accuracy remains stable even for small lesions. This work presents a proof of concept for incorporating reliability into deep learning segmentation pipelines. Ensemble-based confidence estimation improves interpretability and allows clinicians to identify both accurate and uncertain regions. These insights are crucial for the clinical translation of AI tools in breast imaging.