Early and precise detection of prostate cancer using Magnetic Resonance Imaging (MRI) remains a significant challenge in medical research. Despite the promising potential of Deep Neural Networks (DNNs) for prostate cancer screening, ensuring their reliability is crucial. Accurately quantifying prediction uncertainty in diagnoses is imperative in clinical settings. In this study, we introduce a deep learning model designed not only to detect prostate cancer but also to quantify prediction uncertainty, thus distinguishing between confident and uncertain predictions. Our approach uses a 3D DenseNet-121 backbone for feature extraction and Monte Carlo Dropout (MCD) to approximate Bayesian inference, allowing us to estimate the uncertainty in the model’s predictions. We evaluated the model on data from 157 patients, analyzing its reliability and performing an ablation study across different MRI sequences. The model achieved an Area Under the Curve (AUC) of 0.79 across all MRI sequences. In the optimal setup, it classified \(75\%\) of predictions as certain and \(25\%\) as uncertain, with an AUC of 0.9 for certain predictions. These results clearly demonstrate the model’s efficacy in accurately quantifying the reliability of its classifications. By automatically identifying uncertain cases, our approach enables radiologists to focus their attention on these, potentially reducing their workload while enhancing diagnostic accuracy.