Limited funding hinders endangered species recovery. Thus, decision makers need to strategically allocate resources to save the most species. Decision science provides guidance on efficient prioritization of conservation actions. However, endangered species recovery cost estimates are incomplete, so decision makers need to understand the implications of different cost estimation approaches. To test how different ways of estimating the expected costs of recovery action influence suggested recovery priorities, I used three different cost estimation scenarios for prioritizing recovery effort for 29 endangered species in Arizona. My scenarios explored “remaining” costs, calculated by subtracting historical spending from recovery plan cost estimates, “average” costs which substituted the average cost for actions in recovery plans, and “micro” and “macro” overlaps accounting for efficiency of costs due to implementing shared recovery actions for species with overlapping ranges. These different methods of estimating costs resulted in different numbers of recovery plans funded. At a representative budget, the macro overlap scenario recommended funding for 97% of plans as compared to 93% of plans under the baseline cost scenario. In contrast, the micro overlap (59%), the average (28%), and remaining (24%) cost estimation approaches all resulted in less plans recommended for funding than the baseline. There were also differences in how individual plans were ranked across the scenarios and variation in species chosen for funding. The order of recovery plans was similar between the baseline and the remaining scenario (WS = 0.833), and the baseline and the average scenario (WS=0.811). The similarity metric is based on the identity of species ranked equally. In contrast, there was less similarity in plan ranking between the baseline, the macro (WS=0.777), and micro (WS=0.442) overlap scenarios. A group of 4 plans remained within the top priority ranks, 5 plans were ranked as high priority for all scenarios except the remaining cost scenario, and 5 plans were consistently ranked as low priority. My results show how cost estimation approaches influence species priority rankings and can be used to help decision makers determine implications when they are exploring options for prioritization.