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Until recently mass-mapping techniques for weak gravitational lensingconvergence reconstruction have lacked a principled statistical framework uponwhich to quantify reconstruction uncertainties, without making strongassumptions of Gaussianity. In previous work we presented a sparse hierarchicalBayesian formalism for convergence reconstruction that addresses thisshortcoming. Here, we draw on the concept of local credible intervals (cf.Bayesian error bars) as an extension of the uncertainty quantificationtechniques previously detailed. These uncertainty quantification techniques arebenchmarked against those recovered via Px-MALA - a state of the art proximalMarkov Chain Monte Carlo (MCMC) algorithm. We find that typically our recovereduncertainties are everywhere conservative, of similar magnitude and highlycorrelated (Pearson correlation coefficient $\geq 0.85$) with those recoveredvia Px-MALA. Moreover, we demonstrate an increase in computational efficiencyof $\mathcal{O}(10^6)$ when using our sparse Bayesian approach over MCMCtechniques. This computational saving is critical for the application ofBayesian uncertainty quantification to large-scale stage IV surveys such asLSST and Euclid.

Sparse Bayesian mass-mapping with uncertainties: local credible intervals