Deep learning techniques have significantly advanced mineral prospectivity mapping (MPM) by facilitating automated feature extraction and capturing nonlinear relationships among multi-source geological datasets. However, most deep learning models in MPM neglect the intrinsic uncertainties arising from incomplete geological knowledge, limited sampling, and model variability, leading to overconfident and potentially unreliable predictions. To address this limitation, this study proposes a comprehensive uncertainty quantification framework that jointly evaluates data, model, and prediction uncertainties in deep learning-based MPM. Data uncertainty, originating from sparse of geochemical/geophysical sampling and subjective interpretations of geological features, is characterized through stochastic simulation of evidential layers. Model uncertainty, arising from variability in network architecture and parameters estimation, is captured through a Bayesian convolutional neural network (CNN) employing Monte Carlo Dropout. The proposed framework is demonstrated through a real-world case study of gold prospectivity mapping in western Henan Province, China. These uncertainties are quantified using statistical measures including mean, variance, and entropy. The results indicate that areas exhibiting high prospectivity and low uncertainty represent robust and reliable exploration targets, whereas those with high uncertainty highlight regions requiring improved metallogenic interpretation or model refinement. Furthermore, uncertainty contribution analysis reveals that data uncertainty contributes more to total prediction uncertainty than model uncertainty, suggesting that enhancing the quality and representativeness of evidence layers is more effective for reducing uncertainty than merely optimizing network architecture or parameters. Overall, by modeling and visualizing both data and model uncertainties, the proposed framework transforms deep learning-based MPM from deterministic prediction to probabilistic decision-making, thereby enabling more reliable and trustworthy mineral exploration.