Scheduling real-time workflows running in the Cloud often need to deal with uncertain task execution time and minimize uncertainty propagation during the workflow runtime. Efficient scheduling approaches can minimize the operational cost of Cloud providers and provide higher guarantee of the quality of services (QoS) for Cloud consumers. However, most of the existing workflow scheduling approaches are designed for the individual workflow runtime environments that are deterministic. Such static workflow schedulers are inadequate for multiple and dynamic workflows, each with possibly uncertain task execution time. In this paper, we address the problem of minimizing uncertainty propagation in real-time workflow scheduling. We first introduce an uncertainty-aware scheduling architecture to mitigate the impact of uncertainty factors on the quality of workflow schedules. Then we present a dynamic workflow scheduling algorithm (PRS) that can dynamically exploit proactive and reactive scheduling methods. Finally, we conduct extensive experiments using real-world workflow traces and our experimental results show that PRS outperforms two representative scheduling algorithms in terms of costs (up to 60%), resource utilization (up to 40%) and deviation (up to 70%).