A traditional ensemble approach to predicting the remaining useful life (RUL) of equipment and other assets has been constructing data-driven and model-based ensembles using identical predictors. This ensemble approach may perform well on quality data collected from laboratory tests but may ultimately fail when deployed in the field because of higher-than-expected noise, missing measurements, and different degradation trends. In such work environments, the high similarity of the predictors can lead to large under/overestimates of RUL, where the ensemble is only as accurate as the predictor which under/overestimated RUL the least. In response to this, we investigate whether an ensemble of diverse predictors might be able to predict RUL consistently and accurately by dynamically aggregating the predictions of various algorithms which are found to perform differently under the same conditions. We propose improving ensemble model performance by (1) using a combination of diverse learning algorithms which are found to perform differently under the same conditions and (2) training a data-driven model to adaptively estimate the prediction weight each predictor receives. The proposed methods are compared to three existing ensemble prognostics methods on open-source run-to-failure datasets from two popular systems of prognostics research: lithium-ion batteries and rolling element bearings. Results indicate the proposed ensemble method provides the most consistent prediction accuracy and uncertainty estimation quality across multiple test cases, whereas the individual predictors and ensembles of identical predictors tend to provide overconfident predictions.