Accurate segmentation of infarcted myocardium and microvascular obstruction (MVO) in late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging is critical for risk assessment in myocardial infarction patients. However, the task is challenging due to anisotropic CMR resolution, complex enhancement patterns, and severe class imbalance. In this work, we propose a cascaded deep learning framework that combines a 2D slice-wise segmentation network with a 3D correction network to provide enhanced voxel-wise uncertainty estimation. We introduce a novel uncertainty estimation approach that leverages disagreement between the 2D and 3D models as a proxy for segmentation uncertainty. We quantify this via a Soft Correction Score (SCS), based on probabilistic discrepancies, and a Discrete Correction Map (DCM), which encodes interpretable label corrections between networks. We evaluate our framework on the publicly available EMIDEC dataset and on a large in-house clinical dataset. Across both datasets, our framework achieves superior segmentation accuracy and provides uncertainty estimates comparable to established methods such as Monte Carlo Dropout, test-time augmentation, and deep ensembles. The proposed uncertainty measures correlate strongly with prediction errors and offer interpretable insights into ambiguous regions, enhancing both the reliability and clinical utility of automated LGE CMR analysis.