Online remaining-useful-life (RUL) estimation is an effective method with respect to ensuring the safety of complex-huge systems. Generally, current methods assume a specific degradation model when degradation values are observed in the initial degradation phase. However, this assumption may not always be robust enough owing to the often-ambiguous inherent incipient-degradation characteristic. Therefore, besides model-parameter uncertainty, the uncertainty of the degradation model is worth examining in online RUL estimations. In this paper, a Bayesian-updated expectation-conditional-maximization (ECM) algorithm is adopted to address the uncertainty of prior parameters, and a modified Bayesian-model-averaging method is used to deal with the uncertainty of the degradation model. Then, simulation studies are conducted to analyze the effectiveness of the proposed fusion algorithm. Results suggest that the Bayesian-updated ECM algorithm and modified Bayesian-model-averaging method effectively address the associated uncertainties of model parameters and the degradation model itself. Finally, we apply the proposed fusion algorithm to predict the RUL of a gyroscope.