Remaining Useful Life (RUL) estimation is the problem of inferring how long a certain industrial asset is going to operate until a system failure occurs. Deploying successful RUL methods in real-life applications would result in a drastic change of perspective in the context of maintenance of industrial assets. In particular, the design of intelligent maintenance strategies capable of automatically establishing when interventions have to be performed has the potential of drastically reducing costs and machine downtimes. In light of their superior performances in a wide range of engineering fields, Machine Learning (ML) algorithms are natural candidates to tackle the challenges involved in the design of intelligent maintenance approaches. In particular, given the potentially catastrophic consequences associated with wrong maintenance decisions, it is desirable that ML algorithms provide uncertainty estimates alongside their predictions. In this work, we propose and compare a number of techniques based on Gaussian Processes (GPs) that can cope with this aspect. We apply these algorithms to the new C-MAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset from NASA for aircraft engines. The results show that the proposed methods are able to provide very accurate RUL predictions along with sensible uncertainty estimates, resulting in more safely deployable solutions to real-life industrial applications.