In this paper, we present findings from a multiyear expert judgment study that comprehensively characterizes uncertainty in estimates of mortality reductions associated with decreases in fine particulate matter (PM(2.5)) in the U.S. Appropriate characterization of uncertainty is critical because mortality-related benefits represent up to 90% of the monetized benefits reported in the Environmental Protection Agency's (EPA's) analyses of proposed air regulations. Numerous epidemiological and toxicological studies have evaluated the PM(2.5)-mortality association and investigated issues that may contribute to uncertainty in the concentration-response (C-R) function, such as exposure misclassification and potential confounding from other pollutant exposures. EPA's current uncertainty analysis methods rely largely on standard errors in published studies. However, no one study can capture the full suite of issues that arise in quantifying the C-R relationship. Therefore, EPA has applied state-of-the-art expert judgment elicitation techniques to develop probabilistic uncertainty distributions that reflect the broader array of uncertainties in the C-R relationship. These distributions, elicited from 12 of the world's leading experts on this issue, suggest both potentially larger central estimates of mortality reductions for decreases in long-term PM(2.5) exposure in the U.S. and a wider distribution of uncertainty than currently employed in EPA analyses.