Accurate disease classification and biomarker discovery remain challenging tasks in biomedicine. Here, we develop and test a practical approach to combining evidence from multiple models when making predictions using selective Bayesian model averaging of probabilistic rules. This method is implemented within a Bayesian Rule Learning system and compared to model selection when applied to twelve biomedical datasets using the area under the ROC curve measure of performance. Cross-validation results indicate that selective Bayesian model averaging statistically significantly outperforms model selection on average in these experiments, suggesting that combining predictions from multiple models may lead to more accurate quantification of classifier uncertainty. This approach would directly impact the generation of robust predictions on unseen test data, while also increasing knowledge for biomarker discovery and mechanisms that underlie disease.