The proposed approach extends the confidence posterior distribution to the semi-parametric empirical Bayes setting. Whereas the Bayesian posterior is defined in terms of a prior distribution conditional on the observed data, the confidence posterior is defined such that the probability that the parameter value lies in any fixed subset of parameter space, given the observed data, is equal to the coverage rate of the corresponding confidence interval. A confidence posterior that has correct frequentist coverage at each fixed parameter value is combined with the estimated local false discovery rate to yield a parameter distribution from which interval and point estimates are derived within the framework of minimizing expected loss. The point estimates exhibit suitable shrinkage toward the null hypothesis value, making them practical for automatically ranking features in order of priority. The corresponding confidence intervals are also shrunken and tend to be much shorter than their fixed-parameter counterparts, as illustrated with gene expression data. Further, simulations confirm a theoretical argument that the shrunken confidence intervals cover the parameter at a higher-than-nominal frequency.