  Bayesian parameter inference is one of the key elements for model selection
in cosmological research. However, the available inference tools require a
large number of calls to simulation codes which can lead to high and sometimes
even infeasible computational costs. In this work we propose a new way of
emulating simulation codes for Bayesian parameter inference. In particular,
this novel approach emphasizes the uncertainty-awareness of the emulator, which
allows to state the emulation accuracy and ensures reliable performance. With a
focus on data efficiency, we implement an active learning algorithm based on a
combination of Gaussian Processes and Principal Component Analysis. We find
that for an MCMC analysis of Planck and BAO data on the $\Lambda$CDM model (6
model and 21 nuisance parameters) we can reduce the number of simulation calls
by a factor of $\sim$500 and save about $96\%$ of the computational costs.
