A novel method to determine probabilistic operational safety bound for rotary-wing unmanned aircraft systems (UAS) traffic management is proposed in this paper. The key idea is to combine a deterministic model for rotary-wing UAS flying distance estimation to avoid conflict and a probabilistic uncertainty quantification methodology to evaluate the risk level (defined as the probability of failure) of separation loss between UAS. The proposed methodology results in a dynamic and probabilistic airspace reservation to ensure the safety and efficiency for future UAS operations. The model includes UAS performance, system updating frequency and accuracy, and weather conditions. Also, the parameterized probabilistic model includes various uncertainties from different sources and develops an anisotropic operational safety bound. Monte Carlo simulations are used to illustrate the operational safety bound determination with a specified risk level (i.e., probability of failure). It is known that uncertainty plays an important role in determining the operational safety bound size, and the proposed methodology provides a simple and efficient quantification of uncertainty impact on the safety bound with a prescribed risk level. It is also providing a useful tool to quantify uncertainty reduction with additional information and measurements in future UAS operations.