Machine learning has contributed to the advancement of maintenance in many industries, including aviation. In recent years, many neural network models have been proposed to address the problems of failure identification and estimating the remaining useful life (RUL). Nevertheless, the black-box nature of neural networks often limits their transparency and interpretability. Interpretability (or explainability) in maintenance refers to the ability of a predictive model to provide insights into its decision-making process for predicting failures or estimating metrics like RUL. Counterfactual Explanations (CFEs) from Explainable AI (XAI) addresses this problem by explaining model decisions through hypothetical scenarios leading to alternative outcomes. A kind of neural network that could benefit from increased interpretability is Bayesian networks. In general, Bayesian models improve interpretability by quantifying uncertainty. However, incorporating Bayesian uncertainty into neural networks adds complexity because we often need a statistical distribution for each network parameter. This study investigates the use of CFEs within a Bayesian framework to achieve two key objectives simultaneously: (1) enhance the interpretability of RUL estimations and (2) improve model accuracy. We generate two types of CFEs: (1) RUL CFEs that increase/decrease the RUL estimation and (2) uncertainty CFEs with reduced estimation uncertainty, which we use to augment the dataset and increase model accuracy. We apply this method to a classical case study, the C-MAPSS dataset, using a Bayesian Long Short-Term Memory (B-LSTM) model. We demonstrate that CFEs can help identify critical features and fine-tune corrective actions to achieve specific outcomes. For example, following a maintenance action that increased the temperature by 1°F, CFEs can reveal that this adjustment extended the equipment's useful life by 30 cycles. This ability to correlate specific actions with effects enhances both decision-making and maintenance efficiency. Additionally, our data augmentation approach results in a 5% improvement in α−λ accuracy for a strict α of 20%. The root mean square error (RMSE) of the B-LSTM model decreases from 9.56 to 8.47 cycles, demonstrating the potential of Uncertainty CFEs to improve accuracy in aircraft maintenance. The code is publicly available at Github.