Divisible workload applications arise in many fields of science and engineering. They can be parallelized in master-worker fashion and relevant scheduling strategies have been proposed to reduce application markspan. Our goal is to developed a practical divisible workload scheduling strategy. This requires that previous work be revisited as several usual assumptions about the computing platform do not hold in practice. We have partially addressed this concern in a previous paper via an algorithm that achieves high performance with realistic resource latency models. In this paper we extend our approach to account for performance prediction errors, which are expected for most real-world performance and applications. In essence, we combine ideas from multiround divisible workload scheduling, for performance, and from factoring-based scheduling, for robustness. We present simulation results to quantify the benefits of our approach compared to our original algorithm and to other previously proposed algorithms.