Multi-omics data, encompassing heterogeneous sources such as genomics, transcriptomics, and proteomics, has become a powerful resource for disease classification in bioinformatics. Fusion mechanisms combine features from multiple modalities into a single joint representation for predictive models to improve classification performance. However, while most studies focus on accuracy, model calibration - how well predicted probabilities match actual outcomes - remains underexplored in the multi-omics setting. Poor calibration can make even accurate models overconfident and unreliable. This study investigates the impact of three fusion strategies, early, intermediate and late, on three state-of-the-art post-hoc calibration techniques. We further introduce dynamic scaling, a regression-based calibration method that estimates instance-specific temperature parameters for more adaptive calibration than dataset-level techniques. Experiments on four benchmark omics datasets evaluate both classification and calibration metrics. Results show that data quality and class imbalance strongly influence performance, and while dynamic scaling often achieves the best calibration, challenges persist for small and imbalanced datasets.