Deep neural networks (DNNs) have become integral to predictive modelling in high-dimensional datasets, such as omics data, yet their overconfidence in predictions poses significant challenges for safety-critical tasks like medical diagnostics. This study investigates whether multimodal fusion of omics data, specifically RNA-Seq and miRNA-Seq, can improve the uncertainty quantification (UQ) and calibration of DNNs when predicting overall survival in cancer patients. Using data from The Cancer Genome Atlas (TCGA) for Ovarian Serous Cystadenocarcinoma (TCGA-OV) and Breast Invasive Carcinoma (TCGA-BRCA), a multimodal DNN architecture with separate feature extraction branches for each modality implemented, followed by concatenation and classification. Predictive uncertainty was quantified using Monte Carlo Dropout (MCD) with metrics such as mean predictive variance, uncertainty ratio, and entropy, while calibration was assessed using Adaptive Calibration Error. Performance evaluation, guided by Balanced Accuracy (BA) and nested cross-validation, revealed mixed results: the multimodal model showed higher predictive variance (i.e. higher uncertainty awareness) and mean BA for TCGA-BRCA but performed worse for TCGA-OV. Although predictive variance for the BRCA dataset carried statistical significance, the lack of significance across other key metrics limited definitive conclusions. These findings suggest that multimodal fusion may increase uncertainty under specific conditions but highlight the need for more sophisticated architectures and comprehensive analyses to validate these effects.