Uncertainty quantification is crucial in deep learning, especially in medical diagnostics, to measure model prediction confidence and ensure reliable clinical decisions. This study introduces a novel conflict-based uncertainty quantification approach, applied as a case study in lung cancer classification, leveraging Dempster-Shafer Theory in conjunction with Deep Ensemble methods. The proposed method aggregates predictions from multiple neural network models using conflict as an uncertainty measure. By converting softmax outputs into Basic Belief Assignments and applying the rule of combination, this conflict-based method effectively quantifies uncertainty: high conflict values indicate predictions requiring expert review, and low values are considered reliable. Evaluations on the LIDC-IDRI dataset and additional 3D biomedical datasets show that the proposed method achieved high accuracy (0.957) and U