Compressed sensing has the potential to address the challenge of simultaneously requiring high temporal and spatial resolution in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), by randomly undersampling the k-space with a predesigned trajectory. However, the traditional variable density (VD) design scheme includes inherent randomness since many probability density functions (PDFs) correspond to a given acceleration factor and one fixed PDF can generate different trajectories. This randomness may translate to an uncertainty in kinetic parameter estimation. We first evaluate how the one-to-many mapping in trajectory design influences DCE parameter estimation when high reduction factors are used. Then we propose a robust design scheme by adaptively segmenting k-space into low- and high-frequency domains considering the specific characteristics for different subjects and only applying the VD scheme in the high-frequency domain. Simulation results demonstrate high accuracy and robustness compared to the VD design.